Colors viewed in an image are dependent on the light that illuminates the subject of the image. Different illuminants will cause differences in the light reflected from the surfaces of the image subject matter and therefore differences in visual response to the reflected light. The human visual system approximately corrects these response differences to a perceived norm. However, when images are captured on media and viewed under a light source different than the source in the image scene, these natural corrections do not take place. Accordingly, recorded images need to be color-balanced to a “normal” light source in order to appear as they would to the natural eye. This balancing or color correction can be performed once the scene illuminant is identified.
There are many known methods for identification of a light source or illuminant in an image scene. However, the conventional correction algorithms assume that all image pixels represent reflecting surfaces. When an image contains self-luminous objects such as sky and other light sources the surface-pixel assumption is violated. When an image contains a significant portion of non-reflective, self-luminous objects, conventional methods will fail and the image illuminant will be incorrectly determined. For example, if an image contains blue sky and the color-balance algorithm assumes that all pixels are reflecting objects, “bluish” pixels could be taken as evidence that the illumination of the scene is bluish. Because a color correction is approximately the opposite hue of the estimated illuminant, the correction for a bluish illuminant would be to shift the image in a yellowish direction. This correction might produce an overly yellowish ground/surface region and a desaturated sky region.
These color correction, color balance or color constancy algorithms generally do not address the question of how to handle images containing luminous objects, which are also referred to herein as self-luminous objects. They have, rather, focused on images where the surface-pixel assumption is satisfied (e.g., uniformly illuminated Mondrian-like images).
It would be advantageous if a method existed that permitted accurate digital image illuminant corrections to be made for images containing significant exception regions, such as self-luminous regions.